A game machine like a slot machine, a coin adjusting machine, an automatic vending machine, etc., have difficulty in discriminating between genuine and spurious coins under an image recognition technique based on a difference in the angle of a coin that results when the coin is inserted into the machine or based on the rotations of a coin made after the coin is inserted.
For example, a method shown in FIG. 1 can be mentioned as a method for discriminating between genuine and spurious coins by use of an image recognition technique. This method photographs a to-be-examined coin that has been inserted into a machine with a CCD camera or the like, then rotates a circular image (to-be-examined circular image) of the to-be-examined coin facing an unspecific direction(angle), thereby generates a new circular image, and compares a plurality of circular images generated in this way with a master circular image used as a criterion.
In greater detail, a to-be-examined circular image K1 facing the direction of (a) of FIG. 1 is first taken, to-be-examined circular images K2, K3, K4, K5, and K6 that are different in direction are then generated by rotating the circular image K1 in such a way as shown in (b) through (f), respectively, of FIG. 1, and these to-be-examined circular images K1, K2, K3, K4, K5, and K6 are compared with a master circular image. If any one (the circular image K6, for example) of the to-be-examined circular images coincides with the master circular image, the to-be-examined coin is regarded as a genuine coin. If none of the circular images K1, K2, K3, K4, K5, and K6 coincides with the master circular image, the to-be-examined coin is regarded as a spurious coin.
However, according to the conventional coin-discriminating method in which a comparison between the to-be-examined circular image and the master circular image is made by rotating the whole of the to-be-examined circular image, image rotation processing is heeded. Therefore, disadvantageously, much time is required for this processing, and a computer with a high processing performance, or the like, is needed because this image rotation processing must perform intricate calculations, such as a sine (i.e., sine function) calculation and a decimal point calculation, for a rotating coordinate transformation.
Additionally, in another coin discriminating method, a comparison between the to-be-examined circular image and the master circular image is made by correcting a rotational direction of the to-be-examined circular image facing an arbitrary direction. However, according to this discriminating method, processing likewise becomes complicated, and, disadvantageously, much processing time is required because the rotational direction of the circular image must be corrected.
Although an employee can discriminate between genuine and spurious coins visually or manually, much time and labor are required.
In the conventional coin discriminating methods mentioned above, since a comparison between the to-be-examined circular image and the master circular image is made by rotating the circular image of the to-be-examined coin or by correcting the rotational direction thereof, the methods are inferior in coin discriminating precision and have a tendency for discriminating. processing to become complicated in improving the precision of discrimination between genuine and spurious coins. Therefore, disadvantageously, more processing time is required proportionately therewith. In other words, if processing time becomes longer, discrimination between genuine and spurious coins will become difficult when a plurality of coins are inserted successively.